Roman Rizzi
534b0df391
In a previous refactor, we moved the responsibility of querying and storing embeddings into the `Schema` class. Now, it's time for embedding generation. The motivation behind these changes is to isolate vector characteristics in simple objects to later replace them with a DB-backed version, similar to what we did with LLM configs.
55 lines
1.2 KiB
Ruby
55 lines
1.2 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Embeddings
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module VectorRepresentations
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class Gemini < Base
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class << self
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def name
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"gemini"
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end
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def correctly_configured?
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SiteSetting.ai_gemini_api_key.present?
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end
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def dependant_setting_names
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%w[ai_gemini_api_key]
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end
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end
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def id
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5
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end
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def version
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1
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end
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def dimensions
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768
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end
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def max_sequence_length
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1536 # Gemini has a max sequence length of 2048, but the API has a limit of 10000 bytes, hence the lower value
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end
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def pg_function
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"<=>"
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end
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# There is no public tokenizer for Gemini, and from the ones we already ship in the plugin
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# OpenAI gets the closest results. Gemini Tokenizer results in ~10% less tokens, so it's safe
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# to use OpenAI tokenizer since it will overestimate the number of tokens.
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def tokenizer
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DiscourseAi::Tokenizer::OpenAiTokenizer
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end
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def inference_client
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DiscourseAi::Inference::GeminiEmbeddings.instance
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end
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end
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end
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end
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end
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